It is known that EGM signals are collected by use of electrodes placed on endocardial or epicardial leads of a device implanted in a patient. These signals, directly related to electrical activities of cardiac cells of the patient, provide much useful information for the purpose of assessing the patient's condition. Hence, after amplifying, conditioning, digitizing and filtering, EGM signals are mainly utilized to control the implanted device and diagnose rhythm disorders requiring, for example, automatic triggering of an antitachycardia, antibradycardia, or interventricular resynchronization therapy.
However, when it comes to analyzing subjectively the cardiac rhythm, e.g., to perform a diagnosis or to readjust the control/operating parameters of an implanted device, practitioners prefer, in practice, to interpret the information given by a surface electrocardiogram (ECG). A surface ECG allows one to visualize in a direct manner, certain determining factors (e.g., QRS width) and thereby assess the evolution of a cardiac failure.
ECG signals are usually recorded over a long period of time through ambulatory practice by Holter recorders. The recorded ECG signals are then further processed and analyzed in order to evaluate the clinical condition of the patient and eventually diagnose whether a cardiac rhythm disorder is present.
The ECG and EGM signals actually have the same signal source, i.e., electrical activities of the myocardium, however they visually appear in much different manners: the EGM collected by the implantable device provides local information on electrical activities of a group of heart cells, whereas the ECG appears in the form of more global information, in particular influenced by the propagation of electrical signals between the myocardium and body surface with certain morphologic and pathologic specificities. Thus, the display of EGM signals is not very useful to a practitioner who interprets ECG signals.
When a patient implanted with a medical device comes to his/her practitioner for a routine visit, two distinct devices are used: an ECG recorder and an external implant programmer. In order to collect ECG signals, the practitioner places electrodes in particular locations of the patient's torso. The ECG signals are collected between predefined pairs of electrodes to define typically twelve derivations of the collected ECG signals. The external programmer is used to control certain operating parameters of the implanted device (e.g., the battery life), download data from the memory of the implantable device, modify the parameters thereof, or upload an updated version of the device operating software, etc.
The visit with the practitioner therefore usually requires two different devices, as well as specific manipulations for placing the surface electrodes and collecting the ECG signals.
Moreover, the use of these two devices requires the patient to come to a specifically equipped center, usually having the consequence that routine visits are spaced farther apart, resulting in a less rigorous follow-up of the patient.
Furthermore, the ECG recording has various drawbacks, notably:                the preparation of the patient which requires a certain time, correlated with a globally increased follow-up cost;        the local irritation of the skin created by fixing of the electrodes in some patients;        the position of the electrodes varies from one visit to another, inducing variations in the reconstructed ECG;        the ECG recording is affected by several parameters that are difficult to control, such as breathing, movements of the patient, as well as the interferences emitted by various external electrical sources.        
In order to overcome such drawbacks, some algorithms have been developed for reconstruction of the ECG based upon EGM signals that are directly provided by the implanted device. Indeed, reconstruction of the ECG based upon EGM signals would:                avoid, during routine visits, having to place surface electrodes and resort to an ECG recorder;        render a patient's visit simpler and quicker, eventually allow performing a routine visit at the patient's home, and subsequently shorten the intervals between successive visits, and improve the patient's follow-up; and        allow a remote transmission of the EGM data recorded by the implanted device, without the intervention of a practitioner or a medical aid.        
Various algorithms for ECG reconstruction based upon EGM signals have been previously proposed.
U.S. Pat. No. 5,740,811 (Hedberg, et al.) proposes to synthesize an ECG signal by combining a plurality of EGM signals by means of a neural network, fuzzy logic, and/or summer circuit, after a learning process by a “feedforward” type algorithm. This technique does not take into account the propagation time delay between the EGM signals and the surface ECG signals leading to a precision loss in the reconstructed ECG signal. Another drawback of such technique is that it does not take into account the varying position of the endocardial leads between the moment of the learning process and that of the use of the device; a change in the heart electrical axis may bias the synthesized ECG signal, generating a misleading ECG signal. A cardiac disorder that is masked by the biased synthesis may not be accurately diagnosed.
U.S. Pat. No. 6,980,850 (Kroll et al.) proposes to overcome this difficulty, by proposing a method of surface ECG reconstruction implementing a matrix transform allowing to render each of the ECG derivations individually. Such transform also allows to take into account several parameters, such as patient's respiratory activity or posture that influence tracking the position of the endocardial leads through space. The proposed reconstruction consists of transforming, through a predetermined transfer matrix, an input vector representative of a plurality of EGM signals into a resulting vector representative of the different ECG derivations. The transfer matrix is learned through averaging several instantaneous matrices based upon ECG and EGM vectors recorded simultaneously over the same period of time.
Although this last technique brings an improvement to that proposed in U.S. Pat. No. 5,740,811, it nevertheless presents certain drawbacks. First, it makes an assumption that there exists a linear relationship between ECG and EGM vectors: such an approximation, though relatively accurate with patients presenting a regular rhythm, leads in some cases to large errors of ECG reconstruction in the presence of atypical or irregular signal morphologies—corresponding to potentially pathologic cases. Second, in the presence of noise, it does not provide a unique solution for appropriately reconstructing the ECG signals.
The U.S. Pat. No. 7,383,080 and U.S. Patent Publication No. 2008/0065161 now issued as U.S. Pat. No. 8,060,198 describes yet another technique for concatenating the ventricular far field signal (distant signal) observed on the atrium electrode on one hand, with the atrial far field signal (distant signal) observed on the ventricular electrode on the other hand to reconstruct an ECG signal. To connect the two signals at their concatenation, the process includes a step of subtracting a shift to avoid a mismatch, then multiplying each signal by a factor to amplify or attenuate it appropriately.
In the case of a patient with a regular rhythm, this concatenation technique is effective because the two far field signals are well separated. However, for a patient with an irregular rhythm, thus potentially pathological, the far field signals are obscured by the P and R waves and cannot be satisfactorily distinguished from each other. In addition, the proposed processing that simply applies a gain and a time shift reconstructs the ECG signals in a very rough approximation format, and thus does not reproduce the exact morphology of ECG signals.
EP 1 902 750 A1 and its U.S. counterpart U.S. Patent Publication No. 2008/0114259(A1) (ELA Medical), now issued as U.S. Pat. No. 8,050,749, describes a technique for reconstruction of ECG using a principal components analysis (PCA) to extract an endocardial vectogram (VGM) from which a surface vectocardiogram is rebuilt (VCG) to obtain, by a reverse transformation, the reconstructed signals of the different ECG derivations. The reconstruction of the VCG from the VGM is made by a learning phase, including use of a neural network.
These various techniques present certain drawbacks, notably because the EGM and ECG signals, even if they have the same origin, have very different characteristics.
Indeed, electrical activities of a heart reflect the spontaneous stimulations due to the ionic currents in the cardiac cells or artificial stimulations produced by the application of an electrical current to these cells. The EGM (or VGM) signals, directly collected by the implanted device on one or several derivations, reflect the electrical potential of the myocardium, whereas the ECG signals correspond to the electrical potential recorded on the body surface, over a certain number of derivations, after propagating from the myocardium to this body surface.
A satisfactory reconstruction of ECG signals from EGM signals implies taking into account the propagation of the electrical phenomena through the patient's body, and the dependence of transmembrane potential of the ionic currents and of the conductivity of the tissue. These phenomena have been modeled in various forms, generally known as “bidomain models”, that are formulated as nonlinear differential equations of the electrical potential, containing linear, quadratic and cubic terms.
But the reconstruction techniques described in the documents cited above rely on a simple linear relationship between EGM and ECG signals, regardless of the physiological knowledge of the bidomain models, with the exception of techniques using neural networks, which introduce non-linear relationships between the EGM and ECG signals. However, the non-linearity introduced by the neural network is simply a sigmoid function or a limiter, and it is very different from the physiological non-linear bidomain model, reflected by the presence of a quadratic term and a cubic term.
Another drawback, specific to all these techniques is that they can not verify that the reconstruction of ECG signal gives a correct result, and even less quantify the quality of this reconstruction.